Four years of multi-modal odometry and mapping on the rail vehicles

Precise, seamless, and efficient train localization as well as long-term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety (RAMS) engineering for railroad systems. Simultaneous localization and mapping (SLAM) is right at the core of solving the two problems concurrently. In this end, we propose a high-performance and versatile multi-modal framework in this paper, targeted for the odometry and mapping task for various rail vehicles. Our system is built atop an inertial-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, optionally satellite navigation and map-based localization information with the convenience and extendibility of loosely coupled methods. The inertial sensors IMU and wheel encoder are treated as the primary sensor, which achieves the observations from subsystems to constrain the accelerometer and gyroscope biases. Compared to point-only LiDAR-inertial methods, our approach leverages more geometry information by introducing both track plane and electric power pillars into state estimation. The Visual-inertial subsystem also utilizes the environmental structure information by employing both lines and points. Besides, the method is capable of handling sensor failures by automatic reconfiguration bypassing failure modules. Our proposed method has been extensively tested in the long-during railway environments over four years, including general-speed, high-speed and metro, both passenger and freight traffic are investigated. Further, we aim to share, in an open way, the experience, problems, and successes of our group with the robotics community so that those that work in such environments can avoid these errors. In this view, we open source some of the datasets to benefit the research community

Development of UAV-Based Rail Track Geometry Irregularity Monitoring and Measuring Platform Empowered by Artificial Intelligence

Rail tracks need to be consistently monitored and inspected for problems associated with rust, deformation, and cracks that, at their worst, can cause catastrophic train derailments. Many non-destructive testing approaches have been explored and extensively utilized to help inspect rails’ health, but most of them require intensive human power and/or heavy sensor systems (e.g. total stations, manual/car-mounted trolly, etc.) that are not efficient or convenient to cover a long range of rails and may interfere with the normal operation of trains.In light of the rapid development of unmanned aerial systems/vehicles (UAS’s/UAVs) and high definition photographic and optical distance measuring sensors, this paper proposes a novel UAV-based rail track irregularity monitoring and measuring platform that can remotely inspect the geometry irregularity of tracks at various angles and cover a long distance by only a few personnel. By mounting a light distance and range (LiDAR) scanning sensor and a data acquisition system on the UAV, we can continuously collect 3D point cloud data (PCD) frames that reflect the surfaces of tracks, ground, and other objects. Data points in these PCD frames are manually annotated into two classes: rail tracks and background. Then, annotated PCD frames are pre-processed and fed to train a state-of-the-art machine-learning-based 3D point cloud semantic segmentation network, RandLA-Net, to assign each point into one of the two aforementioned classes, so that point clusters that represent rail tracks can be extracted. The trained model can be deployed for real-time distinction between rails and background. Then, principal component analysis (PCA) and multiple regressions are conducted to identify the top and inner surface of the rails. In the end, various geometry measurement of rails, such as gauge, cross level, etc. can be performed to inspect any irregularities. The geometry measurement obtained by the proposed UAV-LiDAR-based framework is compared against standard official value of each geometry. The evaluation results have confirmed the similar or the more advanced performance of the proposed platform with more terrain flexibilities

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck/onboard GNSS receiver/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required

Desenvolvemento de modelos de información de infraestructuras segundo estándares abertos e parametrización automática a partir de datos xeomáticos.

It seeks to develop procedures that allow generating information models of these structures, created from the relevant information of the point clouds obtained with these systems. For this purpose, the BIM standards for civil engineering structures, both currently available and those that will be published for the duration of the thesis, will be exploited and adopted. Information modeling techniques will be used in these standards, with the aim of obtaining a system that allows modeling the structures automatically. The models will also be made compatible with other methodologies designed for BIM, whose purpose is to take full advantage of the information available for management and maintenance tasks. Meeting these objectives, an automatic modeling system will be developed according to the BIM standards for transport infrastructures, suitable for automatic feeding from geomatic data and remote sensing, which is in turn integrable into management and maintenance systems for these types of structures of civil engineering.Esta tesis busca el desarrollo de metodologías para la exportación de la información geomática de infraestructuras de transporte, particularmente estructuras ferroviarias y carreteras, obtenida mediante tecnologías de mapeado móvil. Se busca desarrollar procedimientos que permitan generar modelos de información de estas estructuras, creados a partir de la información relevante de las nubes de puntos obtenidas con estos sistemas. Con este propósito, se explotarán y adoptarán los estándares BIM para estructuras de ingeniería civil, tanto los actualmente disponibles como aquellos que serán publicados durante la duración de la tesis. Se utilizarán técnicas de modelado de información en estos estándares, con objetivo de obtener un sistema que permita realizar un modelado de las estructuras de manera automática. Se llevará a cabo también la compatibilización los modelos con otras metodologías diseñadas para BIM, cuyo propósito es el aprovechamiento total de la información disponible para tareas de gestión y mantenimiento. Cumpliendo estos objetivos se desarrollará un sistema automático de modelado según los estándares BIM para infraestructuras de transporte, apto para su alimentación automática a partir de datos geomáticos y teledetección, el cual es a su vez integrable en sistemas de gestión y mantenimiento para este tipo de estructuras de ingeniería civil.Esta tese busca o desenvolvemento de metodoloxías para a exportación da información xeomática de infraestruturas de transporte, particularmente estruturas ferroviarias e estradas, obtida mediante tecnoloxías de mapeado móbil. A tese busca o desenvolvemento de procedementos que permitan xerar modelos de información destas estruturas, creados a partir da información relevante das nubes de puntos obtidas con estes sistemas. Con este propósito, se explotarán e adoptarán os estándares BIM para estruturas de enxeñería civil, tanto os actualmente dispoñibles como aqueles que serán publicados durante a duración da tese. Utilizaranse técnicas de modelado de información nestes estándares, con obxectivo de obter un sistema que permita realizar un modelado das estruturas de maneira automática. Levarase a cabo tamén a compatibilización dos modelos con outras metodoloxías diseñadas para BIM, cuxo propósito é o aproveitamento total da información dispoñible para tarefas de xestión e mantemento. Cumplindo estes obxectivos se desenvolverá un sistema automático de modelado segundo os estándares BIM para infraestruturas de transporte, apto para a súa alimentación automática a partir de datos xeomáticos e teledetección, o cal é a súa vez integrable en sistemas de xestión e mantemento para este tipo de estruturas de enxeñería civil

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways